Instead, we can also collect all the best matched keys at every hop to construct a Structured Query and execute it over the KB to obtain all qualified answers, namely the SQ approach.

the structured query can be constructed as: we select the keys that have the highest relevance probabilities in every hop, resulting a sequence of keys sk 0 ,....


To understand why, consider the set of premises in Figure 1, which entail the hypothesis H c .

the combined information in P 1 and P 3 entails H c , which corresponds to the correct answer Cambridge.


A sentence is considered important if it contains information that is relevant to answering the question.

the importance of a sentence can be modeled as its entailment probability, i.e., how well the sentence by itself supports the answer hypothesis.


Gendered Language Gendered language is the use of words and phrases that discriminate 1 the gender of a subject.

the gender of mentioned person should be easy to predict from context if the text is gendered.


Since word-affect associations are highly domain dependent, we tailored an existing affect lexicon, the NRC Emotion Lexicon (Mohammad and Turney, 2013), to our domain via label propagation (Hamilton et al., 2016).

we stem all the words in the lexicon and select 8-10 representative stems per emotion category that have an association with that emotion in the context of mass shootings.


They are semantically similar, and it is beneficial to leverage head relational knowledge and transfer it to the long-tail relation, thus enhancing general performance.

long-tail relations of one entity tuple can have class ties with head relations, which can be leveraged to enhance RE for narrowing potential search spaces and reducing uncertainties between relations when predicting unknown relations (Ye et al., 2017).


To address the problem of learning relational knowledge, as shown in (Lin et al., 2016;Ye et al., 2017), we use class embeddings to represent relation classes and utilize KG embeddings and graph convolution networks (GCNs) to extract implicit and explicit relational knowledge.

previous studies  have shown that the embeddings of semantically similar relations are located near each other in the latent space.


However, because there are many one-to-multiple relations in KGs, the relevant information for each class may be scattered.

there may not be enough relational signal between classes.


For example, the relation /location/br state/ has only one sub-relation /location/br state/capital, which indicates that it is more important.

if the sentence has high attention weights on relation /location/br state/, it has a very high probability to have relation /location/br state/capital.


There are 163,108 positive sentences in total, in which 161,392 ones contain entity pairs that related to each other in their descriptions at least once.

over 98.9% instances in positive set fitting our assumption indicates that most entity pairs in positive instances contain each other in their descriptions.


It evaluates our models by comparing relation facts discovered from the test sentences with those in Freebase.

we report both the aggregate Precision-Recall (PR) curves and Precision at top N predictions (P@N) in our experiments.


Therefore, for the t-th iteration, we use a tensor V t R ∈ R b R ×b R ×L R to capture scores of each of the L R relation types.

each edge in the relation graph connects two entity spans s i and s j in the relation beam as the input.


Relation propagation significantly benefits both entity and relation extraction in both domains.

there are a large portion of sentences with multiple relation instances across different entities in both ACE05 and Sci-ERC, which is the scenario in which we expect relation propagation to help.


Nonetheless, this feature is extremely important; given two sentences with equivalent meaning, we would expect a good model to produce two outputs with equivalent meaning.

any meaningpreserving perturbation that results in the model output changing drastically highlights a fault of the model.


chrF: (Popović, 2015) is based on the character n-gram F-score.

we will use the chrF2 score (based on the F2-score -recall is given more importance), following the recommendations from Popović (2016).


Si seulement je pouvais me muscler asusi (typo) rapidement.

for a word-based translation model M 6 , and given an input sentence w 1 , .


* " indicates that the correlation is significantly better than the next-best one.

table 8 shows that the good correlation of chrF with human judgment is not only due to the ability to distinguish between different number of edits.


Among such a large set of possible actions, at most N actions are selected if the length of the generated sentence is N , where we can assume N |V |.

most of the possible actions are not selected, and the large action space slows down reinforcement learning and consumes a large amount of GPU memory.


We assume that a hidden stateh i ∈ R 2d is computed for each x i by using a bi-directional RNN with LSTM units (Graves and Schmidhuber, 2005).

,h i is the concatenation of computed by a pair of forward and backward RNNs.


For both of the tasks, we use the weight-tying technique (Inan et al., 2017;Press and Wolf, 2017) by using W p as the word embedding matrix.

e(y t ) is the y t -th row vector in W p , and the technique has shown to be effective in machine translation (Hashimoto and Tsuruoka, 2017) and text summarization (Paulus et al., 2018).


However, for example, at most 100 unique words are selected when generating a sentence of length 100.

the output length N is much smaller than the vocabulary size |V |, and this fact motivated us to reduce the large action space.


To accelerate the training, we propose to construct a small action space for each source input.

our method selects a small vocabulary V of size K for each source input in advance to the model training.


Such a joint training approach requires the model to continuously update the vocabulary predictor during RE-INFORCE, because the encoder is shared.

the action space for each input changes during reinforcement learning, and we observed unstable training.


In the training with the REINFORCE algorithm, the speedup by our method is enhanced.

in the En-Ja (2M) experiments, our method gains a factor of 2.7 speedup compared with the full softmax baseline (S = 3).


These results show that our method easily fits in the memory of the single GTX 1080 GPU, whereas "Full softmax" is very sensitive to the vocabulary size |V | and the sentence lengths.

we observe about 56% reduction in memory usage when using the En-Ja (2M) dataset.


Extensive experiments on real-world data sets demonstrate that our method can achieve a considerable improvement in overall prediction accuracy compared to existing approaches.

our model improved the accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in "20NewsGroup" data.


• Metric learning in feature representation is designed to boost the performance of the dropout-based uncertainty methods in the text classification task.

the shortened intra-class distance and enlarged inter-class distance can reduce the prediction variance and increase the confidence for the accurate predictions.


• A new dropout-entropy method based on the Bayesian approximation property of Dropout in DNNs is presented.

we measure the model uncertainty in terms of the information entropy of multiple dropout-based evaluations combined with the de-noising mask operations.


• Extensive experiments on real-world data sets demonstrate that the effectiveness of our proposed approach consistently outperforms existing methods.

the macro-F1 score can be increased from 0.78 to 0.92 by assigning 25% of the labeling work to human experts in a 20-class text classification task.


To achieve this, we use metric learning to train the desirable embeddings.

let r i and r j be the feature representations of instances i and j, respectively, then the Euclidean distance between them is defined as , where d is the dimension of the feature representation.


", we measure the classification performance in terms of various uncertainty ratios.

assuming the entire testing set S has size n and an uncertainty ratio r, we can remove the most uncertain samples S r from S based on the uncertainty ratio r, where the size of the uncertainty set S r is r • n. We assume the uncertain samples S r handed to domain experts achieve 100% accuracy.


3) The DE method outperforms all the other methods when metric learning is not applied.

dE is around 4% better than the dropout method in terms of the Micro-F1 score.


Following previous work (Zhang and Lapata, 2017;Zhao et al., 2018), we use SARI as our main automatic metric for evaluation (Xu et al., 2016).

11 SARI calculates how often a generated sentence correctly keeps, inserts, and deletes n-grams from the complex sentence, using the reference simple standard as the gold-standard, where 1 ≤ n ≤ 4.


In this paper we follow the same line of study: We consider how to adapt RNN sequence model to multi-label set prediction without specifying the label order.


If only some sequence permutations receive high probabilities while others receive low probabilities, the set probability computed as the product of sequence probabilities will still be low.

if for each document, RNN finds one good way of ordering relevant labels (such as hierarchically) and allocates most of the probability mass to the sequence in that order, the model still assigns low probabilities to the ground truth label sets and will be penalized heavily.


However, they neglect the useful inherent information of those sentences which should be replaced correctly.

they remove the noise rather than utilize it in the right way.


Thus, given two analogous entity pairs represented by their textual mentions sets as input, the model is trained to minimize the difference between the representations of relations having the same linguistic patterns.

the model learns the different paraphrases expressing the same relation.


Given two pairs of entities, (A, B) and (C, D), their semantic relations can be expressed by their mentions in text, (A, B) = {S i } and (C, D) = {S j }.

{S i } and {S j } are the sets of sentences where (A, B) and (C, D) co-occur in the same set.


Our idea is to train a neural network to solve the analogy problem between any two entity pairs in this dataset, as long as they are described by the textual contexts where they co-occur.

this task is reduced to a binary classification of determining whether the relational similarity between the representations of two sets of mentions exceeds a threshold.


Thus, the use of the contexts surrounding the two entities in the mentions sets to compute the relational similarity score is needed.

we cast the task of oneshot relational classification to a problem of measuring textual (i.e.


To understand the empirical strengths and weaknesses of these approaches, we compare the use of SVCCA and diagnostic classifiers in understanding learning dynamics.

we ask: is our first conceptual shift (to SVCCA) necessary?


This line shows the test PCC scores (y-axis) of SIF encoder on the news corpus when the number of training corpora increases (xaxis).

the left-most blue dot indicates the test result of SIF encoder on news corpus when trained on news corpus itself (that is, the first training corpus is used); the second point indicates the test results of SIF encoder on news corpus when trained on news and captions corpora (i.e., the first two training corpora are used); the third point indicates the test results of SIF encoder on news corpus when trained on news, captions, and wordnet corpora (that is, the first three training corpora are used), so on and so forth.


But if target relation clusters and the KB share some entities, we can use the shared entities as a bridge to introduce indirect supervision for the relation discovery problem.

we build constraints between pairs of tuples based on the Figure 1: An illustration of our new relation discovery setting.


Our model is a relation extractor to predict the underlying semantic relation r ∈ R X given sentences X , with the help of G(E G , T G ).

we focus on the challenging scenario where In this section, we first review the discrete-state variational autoencoder (DVAE) in §3.1.


Instead of modeling the common relation paths or logic rules, which is computationally infeasible, we compare them in the latent embedding space.

we model the KB using the TransE (Bordes et al., 2013) model, where a relation is interpreted as a translation from the head entity to the tail entity, with a score function, e 1 + r = e 2 for each gold triplet (e 1 , r, e 2 ) in the KB.


We also note that the correlations are higher for the inference network than for gradient descent, showing the impact of amortization during learning of the inference network parameters.

since we are learning to do inference from a dataset, we would expect search error to be smaller for more frequent sequences, and we do indeed see this correlation.


Letx = x tn...T −1 be the output sentence generated by adversarial agent in the speaking model after n -th turn.

we define x 0...T −1 as all the tokens in the dialogue history before selection .


While we could let our adversarial agent and the target agent negotiate n turns, it is still unknown which n should be chosen to get the best performance.

we aim to not only know what to say but also when to say to fool the target agent.


[2018] for training, i.e., incorporate binary cross-entropy loss over the triple scores.

for subjectrelation pairs (s, r) in the training data G, we use binary y s,r o to represent negative and positive facts.


Previous work have used this concept of influence similarly for several different tasks [Kononenko et al., 2010, Koh andLiang, 2017]  where We are also interested in investigating the robustness of models, i.e., how sensitive are the predictions to small additions to the knowledge graph.

for a target prediction s, r, o , we are interested in identifying a single fake fact s , r , o that, when added to the knowledge graph G, changes the prediction score ψ(s, r, o) the most.


First, evaluating the effect of changing the KG on the score of the target fact (ψ(s, r, o)) is expensive since we need to update the embeddings by retraining the model on the new graph; a very time-consuming process that is at least linear in the size of G. Second, since there are many candidate facts that can be added to the knowledge graph, identifying the most promising adversary through search-based methods is also expensive.

the search size for unobserved facts is |ξ| × |R|, which, for example in YAGO3-10 KG, can be as many as 4.5M possible facts for a single target prediction.


Further, calculating the effect of the attack on e s and e r requires a third order derivative of the loss, which is not practical (O(n 3 ) in the number of parameters).


We design a network, shown in Figure 2, that maps the vector z s ,r to the entity-relation space, i.e., translating it into (s , r ).

we train an auto-encoder where the encoder is fixed to receive the s and r as one-hot inputs, and calculates z s,r in the same way as the DistMult and ConvE encoders respectively (using trained embeddings).


Intuitively, if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple.

the error triple should have the least influence on the model's prediction of the training data.


we will evaluate the impact of the different methods intended to perform structured inference ( §3.4).


If there is more than one coordinate structure in a sentence, each coordinate structure must be isolated from the others or integrated into the other(s).

coordinate structures cannot be partially overlapped.


To overcome these limitations, we use the CKY algorithm to find the optimal coordinations in a sentence.

we define the CFG rules to produce a coordinate tree, as used in Hara et al.


(2017)'s model to directly capture interaction among multiple predicate-arguments.

the model improved the performance of arguments that have no syntactic dependency with predicates and achieved a state-of-the-art result on Japanese PASA.


Multi-ALL+DEP achieved the best F1 score among all the models including previous state-of-the-art models.

the dependency feature was effective for Dep arguments.


In Example (f), the Multi-ALL+DEP model correctly predicted the answer, but the Multi-ALL model failed.

multi-ALL+DEP correctly predicted that the ACC argument is yakuwari "role," which is dependent on hatasu "play."


As we have mentioned before, we believe there exist Chinese address-specific characteristics associated with address texts that can be exploited in designing a parsing model.

we argue there are two types of structured information within Chinese addresses that can be exploited when designing our parser -the latent tree structures and the regular chain struc-tures.


Such auxiliary labels are assigned to the internal nodes within a parse tree.

for each original label X, we introduce the auxiliary label X.


This motivates us to employ an alternative representation for capturing dependencies within chunks that appear at the beginning of the addresses, which are believed to exhibit more regular patterns.

we employ a chain structure to capture the dependencies between adjacent labeled chunks.


Like prior work (Malmi et al., 2018), we observed a skewed distribution of discourse phenomena in the data.

examples with anaphora or the connectives "and" and "but" constitute 99.7% of Sports and 59% of Wikipedia examples.


But in cases where raters agree to add a connective, agreement figures increase substantially.

when it is clear that a connective is needed, there is also high agreement for picking the right one (76%), for deciding whether  to add an anaphor (70%), and for deciding whether to merge the sentences or not (70%).


Pre-training with DFWIKI improves SARI score by 9% compared to using WEBSPLIT alone.

the F1 of the 'kept' and 'added' n-grams is significantly higher, by 23% and 33% respectively.


In particular, the F1 of the 'kept' and 'added' n-grams is significantly higher, by 23% and 33% respectively.

'added' tokens refer also to correctly choosing discourse connectives, to which the large-scale examples in DISCOFUSE were likely helpful.


The second component of our model is designed to make the more specific responses more semantically plausible.

we found that forcing a SEQ2SEQ model to be more specific exposes problems with plausibility as illustrated in Table 1.


NIWF is defined in Section 3.3. we learn embeddings of various specificity levels for each metric jointly with the model.

for each metric m, we rank the responses in the training data according to that metric and divide it into K = 5 levels of equal size.


We use classifiers targeting various types of errors using synthetically generated data.

we train four classifiers that distinguish true response sentences from the dataset and negative sentences we create that reflect a specific type of semantic implausibility: Phrase compatibility: We split all the training data into phrases by splitting sentences on punctuation or discourse connectives.


This result shows that using local context to predict description is more effective than using it to disambiguate the meanings in global context.

the low BLEU scores of Global and I-Attention models on Wikipedia dataset suggest that it is necessary to learn to ignore the noisy information in global context if the coverage of pre-trained word embeddings is extremely low (see the third and fourth rows in Table 2).


Figure 3(a) shows that the description generation task becomes harder as the phrases to be described become more ambiguous.

when a phrase has an extremely large number of senses, (i.e., #senses ≥ 4), the Global model drops its performance significantly.


Our study will be based on text classification tasks, a broad family of NLP tasks including document classification (DC), natural language inference (NLI), natural language understanding in dialog systems (NLU), etc.

we aim to answer the following questions: 1.


We conduct comprehensive experiments to evaluate the performance of VVD (section 4) on different end classification tasks.

we compare against an array of strong baseline selection algorithms, including the frequency-based algorithm (Luong et al., 2015), TF-IDF algorithm (Ramos et al., 2003), and structure lasso algorithm (Friedman et al., 2010), and demonstrate that it can consistently outperform these competing algorithms by a remarkable margin.


These metrics namely Area Under Curve (AUC) and Vocab@-X% separately measure the vocabulary selection performance globally and locally.

aUC computes enclosed area by the curve, which gives an overview of how well the vocabulary selection algorithm performs.


The main datasets we are using are listed in Table 2, which provides an overview of its description and capacities.

we follow (Zhang et al., 2015;Goo et al., 2018;Williams et al., 2018) to pre-process the document classification datasets, natural language understanding dataset and natural language inference dataset.


The key techniques of our approach are twofold: memoryshared embeddings and a variant of the key-valuequery (KVQ) self-attention mechanism (Vaswani et al., 2017).

our approach reconstructs well-trained word embeddings using a limited number of embedding vectors that are shared by all the subwords with an effective weighting calculated by the self-attention mechanism.


This means that all the problems in the evaluation datasets now became OOV problems.

the number of OOV data in Table 2 in this setting always matches to the evaluation data size, such as 2034 for RW.


Our experiments with monolingual and delexicalized cross-lingual parsing using the Universal Dependency treebank (Zeman et al., 2017) demonstrate the effectiveness of our approach.

our BNNP consistently outperforms the single task BiLSTM baseline (Kiperwasser and Goldberg, 2016), while outperforming the BiAFFINE parser (Dozat et al., 2017) by up to 3% on Vietnamese and Irish.


These approaches have been applied in machine translation (van der Wees et al., 2017), language models (Moore and Lewis, 2010; Duh et al., 2013), dependency parsing (Søgaard, 2011), etc.

ruder and Plank (2017) used Bayesian optimization to select relevant training instances for transfer learning, and Tsvetkov et al.


Thus, in our second stage, we instead apply a nonparametric Bayesian approach to search for the approximately-optimal mixing ratio.

we use a 'Gaussian Process' to sequentially search for the mixing ratio by trading off exploitation and exploration automatically.


Here, we use the automatic relevance determination (ARD) Matern kernel (Rasmussen, 2004), which is parameterized by ν > 0 that controls the level of smoothness.

samples from a GP with such a kernel are differentiable ν − 1 times.


We use the Jensen-Shannon Divergence (JSD) to quantify the difference between two attention distributions: We empirically characterize the relationship between attention weights and corresponding feature importance scores.

we measure correlations between attention and: (1) gradient based measures of feature importance (τ g ), and, differences in model output induced by leaving features out (τ loo ).


In the case of the diabetes task, we again observe a pattern of low JSD for positive examples (where evidence is present) and high JSD for negative examples.

for this task, if one perturbs the attention weights when it is inferred that the patient is diabetic, this does change the output, which is intuitively agreeable.


The second threshold is needed to account for situations where an action must be chosen to advance the quest for which the agent has no prior in G t .

action pruning may remove actions essential to quest completion because those actions involve combinations of entities that have not been encountered before.


The data for the pre-training approach is generated using an oracle, an agent capable of finishing a game perfectly in the least number of steps possible.

the agent knows exactly what action to take given the state observation in order to advance the game in the most optimal manner possible.


The insight into pre-training portions of the agent's architecture is based on converting textadventure game playing into a question-answering activity.

at every step, the agent is asking-and trying to answer-what is the most important thing to try.


In this work, we propose to improve the information aggregation for multi-head attention with a more powerful routing-by-agreement algorithm.

the routing algorithm iteratively updates the proportion of how much a part (i.e.


We propose to empirically validate the importance of information aggregation in multihead attention, by comparing the performance of the standard linear function and advanced aggregation functions on various tasks.

we cast information aggregation as the assigningparts-to-wholes problem (Hinton et al., 2011), and investigate the effectiveness of the routing-byagreement algorithm -an appealing alternative to solving this problem (Sabour et al., 2017;Hinton et al., 2018).


In this work, we cast information aggregation in multi-head attention as the problem of assigningparts-to-wholes.

each attention head extracts different linguistic properties of the same input (Raganato and Tiedemann, 2018), and the goal of information aggregation is to compose the partial representations extracted by different heads to a whole representation.


Both strategies perform better than the standard multi-head aggregation (Row 1), verifying the effectiveness of the non-linear aggregation mechanisms.

the two strategies require comparable parameters and computational speed, but EM routing achieves better performance on translation qualities.


In contrast, generating correct logical forms in our task required modeling sequential dependencies between logical forms.

coreference is modeled between events mentioned in different logical forms by repurposing the copying mechanism originally used for modeling out-of-vocabulary tokens.


To this end, we propose a neural multitask learning framework to incorporate knowledge into citations from the structure of scientific papers.

we propose two auxiliary tasks as structural scaffolds to improve citation intent prediction: 1 (1) predicting the section title in which the citation occurs and (2) predicting whether a sentence needs a citation.


We propose a neural multitask learning framework for classification of citation intents.

we introduce and use two structural scaffolds, auxiliary tasks related to the structure of scientific papers.


We are using the section titles from a larger set of data than training data for the main task as a proxy to learn linguistic patterns that are helpful for citation intents.

we leverage a large number of scientific papers for which the section information is known for each citation to automatically generate large amounts of training data for this scaffold task.


We use a Multi Layer Perceptron (MLP) for each task and then a softmax layer to obtain prediction probabilites.

given the vector z we pass it to n MLPs and obtain n output vectors y (i) : We are only interested in the output y (1) and the rest of outputs (y (2) , ..., y (n) ) are regarding the scaffold tasks and only used in training to inform the model of knowledge in the structure of the scientific documents.


For all four groups, adding pair2vec to 3CosAdd results in significant gains.

the biggest relative improvements are observed for encyclopedic (356%) and lexicographic (51%) relations.


Submodular functions are set functions F : denotes the power set of ground set V. Submodular functions satisfy the following equivalent properties of diminishing returns: ∀X, Y ⊆ V with X ⊆ Y , and ∀s ∈ V \ Y , we have the following.

the value addition due to incorporation of s decreases as the subset grows from X to Y .


We trained the hyperparameters of the models on the validation set using Adam (Kingma and Ba, 2014).

we set the word embedding dimension to be 128, where the word embeddings are initialized randomly, and GRU dimension to be 256.


To demonstrate the generalizability of our persuasion strategies and the resulted semi-supervised model, we also applied our Semi-Att model to 5671 textual requests for pizza from the Reddit community "Random Acts of Pizza" (RAOP).

we used the data released by Althoff et al., (2014) where each request asked for a free pizza and the outcome whether its author received a pizza or not was provided in the dataset.


Here, we consider whether it could be possible to increase the amount of available data by augmenting a corpus in one language with data from another language, and thus improve predictive per-formance without the need for new data collection.

we consider augmenting a relatively small French dataset with a much larger English one.


Another option is to combine the French and English datasets before extracting features.

we first replace the word-level transcripts with the sequence of information units, and then combine the two datasets and train the language models over the multilingual corpus, thus generating multilingual language models.


Moreover, we explore ensemble prediction as an additional aggregation method.

we learn a restricted multiscale model by limiting the text span to a single discourse unit (e.g., a paragraph); the model still combines representations across mentions and subrelations.


Our handcrafted gene-mutation filter also improved all systems substantially, corroborating the analysis of Wang and Poon (2018).

without the filter, it is hard for the document-level models to achieve high precision, so they sacrifice a lot of recall to get good F1 scores.


Such prompts concern the comparison of specific interventions with respect to a particular outcome.

these questions ask whether an article reports that the specified intervention was found (in the described trial) to be significantly more effective than a comparator treatment, with respect to the outcome of interest.


Once this sentence is selected, we use simple checks to try and identify a reported p-value.

we search for the following three distinct forms: "p = X", "p > X", and "p < X"; we use a simple RegEx to find instances of these, ignoring whitespace.


This work draws from recent advances in NLP such as self-attention, hierarchical deep learning models, and contextual dependencies to produce a dialogue act classification model that is effective across multiple domains.

we propose a hierarchical deep neural network to model different levels of utterance and dialogue act semantics, achieving state-of-the-art performance on the Switchboard Dialogue Act Corpus.


Utterance Encoder: Each utterance in the dialog is processed sequentially using single layer Bidirectional Long Short Term Memory (BiLSTM) (Dyer et al., 2015) network and self-attention mechanism (Vaswani et al., 2017) to get the utterance representation.

given an utterance with one-hot encoding for the tokens, u k = {w k,1 , w k,2 , ...., w k,L }, each token is mapped to a vector v k,i = Ew k,i ; i = 1, 2, ...L using pretrained embeddings (matrix E).


They proposed to convert dependency trees to ternary trees, but ignore the type of dependency relations.

they modelled on trees of which the nodes and edges have no labels.


One way is to make explicit decision to select the child by adding a switch variable, at the cost of increasing the complexity of the problem.

instead, we make a change to the second case above, and simplify the problem by restricting the rules to be of the following form: • the heir would inherit both the lexicalization and the label of the parent (with the possible exception that the root node may produce children that are not labeled with "root").


We perform ablation studies on our architecture, with content-based lexicalization.

we consider two alternative ways of making rule prediciton.


On the positive side, we have shown that framing models from an entity-centric perspective makes it very natural to adopt architectural decisions that are good inductive biases.

by exploiting the fact that both speakers and referents are entities, these models can do more with the same model size, improving results on less frequent entities and emulating rule-based behavior such as "a first person expression refers to the speaker".


We found that most hyperparameters could be safely fixed the same way for all three types.

our final models were all trained in batch mode using the Adam optimizer (Kingma and Ba, 2014), with each batch covering 25 scenes given to the model in chunks of 750 tokens.


To our knowledge, the state-of-the-art for namefree domain classification is SHORTLISTER (Kim et al., 2018b), which leverages personalized user provided information for better classification performance.


It is also similar to the one in SHORTLISTER, except we will evolve the module as we are adding new domains.

given dataset D k on k domains for initial training, a domain embedding table T k ∈ R k×ds will be learned where d s is the size of the domain embeddings.


To support online CDA when a new domain is available, we apply a similar approach to the domain embedding expansion described above to expand the prediction layer.

denote W (2) k ∈ R k×d h be the weights for the prediction layer that has been trained on the initial k domains.


To alleviate this problem, we add a new domain embedding regularization term in the loss function to constrain the new domain embedding vector length and force it to direct to a similar area where the known domains are heading towards, so that the new domain will not dominate the domain summarization.


We assume all training data are available to us, and measure the similarity of two domains by comparing their average of utterance hidden representations.

denote ϕ : U → R du as the LSTM-encoder that will map an utterance to its hidden representation with dimension d u .


We vary the number of domains for initial training to see if it will have a big impact on the model performance.

we pick 100 and 500 domains from InitTrain, and use the same IncTrain data for domain adaptation.


Varying the hinge loss thresholds: We vary the classification hinge loss thresholds ∆ pos and ∆ neg to see how it will affect the performance.

we fix ∆ neg = 0.3 and vary ∆ pos from 0.5 to 1.0, and fix ∆ pos = 0.5 and vary ∆ neg from 0 to 0.4, respectively.


We vary the threshold ∆ der to see how it will affect the model performance.

we vary ∆ der from 0 to 0.5 with step size 0.1, and Figure  5 shows the model performance.


To see how the weighted domain similarity loss will affect the performance, we compare it against the plain version without the utterance similarity weights.

we set each λ i = λ dsl having the same value.


Here we compare the two on the regular training setting where we train the model from scratch on a large dataset.

we compare the initial training performance on 100, 500, and 900 domains which are the same as we used earlier.


In this work, we characterize the consistency of dialogue systems as a natural language inference (NLI) (Dagan et al., 2006) problem.

nLI is focused on recognizing whether a hypothesis is inferred from a premise.


In dialogue systems, we cast a generated response as the hypothesis and the conversation history as the premise, projecting thus the automatic evaluation into an NLI task.

we propose directly calculable approximations of human evaluation grounded on conversational coherence and affordance by using state-of-the-art entailment techniques.


Therefore, a dialogue generation task can be formulated as a next utterance prediction problem.

the model predicts a response u i+1 given a conversation history (u 1 , ..., u i ).


In this section, we evaluate the performance of the state-of-the-art entailment models on predicting a score for the generated utterances.

the conversation history H is treated as a hypothesis, whereas the generated response r acts as a premise.


In order to overcome this, we use standard simple models which do not capture all pairwise interactions, but learn to emulate certain characteristics of a complex teacher network.

we first investigate the conicity of representations learned by a complex model and observe that it is significantly lower than that of simpler models.


Such complex span prediction models are also being adapted for other NLP tasks such as dialog response prediction (Moghe et al., 2018), which is the focus of this work.

we refer to the Holl-E dataset where the task is to extract the next response from a document which is relevant to the conversation (see Figure 1).


One principled way of doing this is to use knowledge distillation (Hinton et al., 2015) where the simple model acts as a student and learns to mimic the probability distributions predicted by a teacher.

instead of simply maximizing the log likelihood of the training data, the simple model now gets additional signals from the teacher which act as hints while training.


Our experiments, using the Holl-E dataset show that by (i) improving the conicity (Chandrahas et al., 2018) of the representations learned by the simple model and (ii) mimicking the outputs of the complex teacher model the simple model can give improved performance with fewer compute and memory requirements.

when compared to a standalone simple model the student model shows an improvement of 3.4% (compare SAM-mul-train (LD) and SAM-add-topk (LD) entries in Table 2 and Table 3 respectively).


These models typically differ in the components used for capturing interactions between query and document, capturing interactions between sentences in a document and refining the query/document representation over multiple passes (Shen et al., 2017;Dhingra et al., 2017;.

a co-attention network which computes the importance of every query word w.r.t.


Based on these observations we hypothesize that decreasing the conicity of the vectors h R and h P would improve the performance of the simple attention model.

we propose to change the multiplicative method of combining vectors into an additive method instead.


In particular, we propose to change the multiplicative method of combining vectors into an additive method instead.

we propose to replace the function mul in our simple model with the function add defined as follows: where the vector w and the matrices W i parameterize the mode of combining the input vectors.


We do not view the teacher predicted distribution for all indices with importance and just take the top few predicted indices.

the loss on a single training sample is given below e ) + D(y e ||p S e ) (2) where p T b , p T e gives just the (normalised) probability of the top-k predictions of teacher model on the begin and end indices.


Another possible way to take advantage of teacher models is to extract more information than simply the predicted spans for each training example from the teacher models.

one easy way to extract piece of information is the gradient of the model output with respect to the input for the teacher model.


We propose a middle ground by training a simple model to mimic the characteristics of a complex model.

we make observations from a complex model which learns very diverse representations for different inputs and suitably modify the simple model to learn similar diverse representations.


Our approach significantly outperforms the previous methods on all languages.

compared with the Word-Adv 1 baseline, which only performs word-level adversarial transfer, our approach achieves 10% absolute F-score gain on average, which demonstrates the effectiveness of the sentence-level adversarial transfer.


We use Amharic as the target language and Tigrinya as the source language to show the impact of the size of their annotations.

to explore the impact of the size of target language annotations, we use 0, 10%, 50%, or 100% annotated training data from Amharic.


Similar to the projected dependencies, we extract supervised dominant dependency directions from the gold-standard source treebank D: λ (D) .

when we encounter a gold-standard dependency relation i, m, h, r in a source treebank D, we change the direction if the following condition holds: if the source and target languages do not have the same dominant dependency direction for r and the dominant direction of the target language is the reverse of the current direction, we change the direction of that dependency.


We create a training instance for every modifier word i, m, h, r .

the decision about the direction of each dependency can be made based on the following condition:  we decide about the new order of a dependency according to the mapping µ (i) .


Apart from improved mapping, both cycle consistency and reconstruction lead to more stable training in our experiments.

they help our training to converge and get around the mode collapse issue (Goodfellow, 2017).


3 To show the generality of different methods, we consider European, non-European and low-resource languages.

we evaluate on English (En) from/to Spanish (Es), German (De), Italian (It), Arabic (Ar), Malay (Ms), and Hebrew (He).


Since the source language and the assisting language (English) have different word order, we hypothesize that it leads to inconsistencies in the con- After Reordering  textual representations generated by the encoder for the two languages.

given an English sentence (SVO word order) and its translation in the source language (SOV word order), the encoder representations for words in the two sentences will be different due to different contexts of synonymous words.


In this work, we take a step towards practical "universal" NMT -training massively multilingual models which support up to 102 languages and with up to one million examples per languagepair simultaneously.

we focus on training "English-centric" many-to-many models, in which the training data is composed of many language pairs that contain English either on the source side or the target side.


(2017) for more details on the model architecture.

we use 6 layers in both the encoder and the decoder, with model dimension set at 512, hidden dimension size of 2048 and 8 attention heads.


In all of our multilingual models we use heterogeneous batching, where each batch contains examples which are uniformly sampled from a concatenation of all the language pairs the model is trained on.

we use batches of 64 examples for sequences shorter than 69 tokens and batches of 16 examples for longer sequences.


We find an additional difference between the results on the resource-scarce languages (Table 1) and the higher-resource languages (Table  2).

the bilingual baselines outperform the many-to-one models only in the higherresource setting.


All models are trained and evaluated separately for each dataset by calculating word accuracy over all tokens.

there is no step to discriminate between tokens that require normalization and those that do not; all word forms in the datasets are treated equally.


To facilitate better transfer, we propose to use monolingual information in the form of word co-occurrences in contrast relations, in addition to cross-lingual embeddings (see Figure 1).

we utilize the fact that discourse markers conveying contrast are more likely to be surrounded by antonyms than synonyms (e.g., hot…while…cold), as shown by Roth and Schulte im Walde (2014).


To overcome this difficulty, some approaches (Mrkšić et al., 2017;Glavaš and Vulić, 2018) combine resources for English and cross-lingual word embeddings to distinguish lexical relations in other languages.

they train and test one model across different languages.


Our experiments test the performance of an offthe-shelf system for lexical relation classification under different cross-lingual settings.

we evaluate the performance of unsupervised cross-lingual word embeddings and assess the benefits of translation and re-ranking, taking into account word co-occurrences in contrast relations.


For crosslingual development and hyper-parameter selection, we use the German dataset by Glavaš and Vulić (2018).

we select the number of hidden layers {0, 1}, learning rate {0.001,…,0.1}, dropout rate {0.0, 0.5}, and whether to include information on paths between words {yes, no}.


Compared to the NoTrans baseline, we find BestTrans to substantially increase the number of correctly clas-sified synonyms (by almost 20%).

this affects words derived from the same stem (unzählig/zahllos 'countless'), synonymous verbs (pfuschen/schummeln 'cheat') as well as (near-)synonymous adjectives (verfügbar/verwendbar 'available/usable').


In this work, we show that, on these tasks, polyglot character-based language models can provide benefits on top of those offered by contextualization.

even when crosslingual transfer with word type embeddings hurts target language performance relative to monolingual models, polyglot contextual representations can improve target language performance relative to monolingual versions, suggesting that polyglot language models tie dissimilar languages in an effective way.


We use a state-of-the-art graph-based dependency parser with BiLSTM and biaffine attention (Dozat and Manning, 2017).

the parser takes as input word representations and 100dimensional fine-grained POS embeddings following Dozat and Manning (2017).


We use the same UD treebanks and train/dev./test splits as the CoNLL 2018 shared task on multilingual dependency parsing (Zeman et al., 2018).

we use the GUM treebank for English, 7 GSD for Chinese, and PADT for Arabic.


Table 2 shows that we outperform the state-of-the-art systems from the shared task competition.

our LMs even outperform the Harbin system, which uses monolingual CWR and an ensemble of three biaffine parsers.


Moreover, the absolute position (P E) also improved the performance in this test set.

p E was very effective in the setting of very short headlines (len = 30).


This table indicates that LDP E could control the length of headlines precisely.

lDP E could generate headlines with the identical length to the desired one in comparison with lenInit and lC.


We carry out this strategy to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU.

we propose Scalar Gated Unit (SGU), which is a simplified variant of GRU, and use it to replace the GRUs at the middle layers of HRED and R-NET.


To address this concern, we carry out our proposed strategy to simplify two typical hierarchical recurrent models, namely HRED and R-NET, whose basic building block is GRU.


Since a dialogue is a sequence of sentences, where each sentence is a sequence of words, HRED models this hierarchy with a hierarchical recurrent structure.

hRED consists of three layers of GRUs, which from bottom to top separately serve as the sentence-level encoder, the dialogue-level encoder, and the decoder.


R-NET is an end-to-end MRC model that predicts an answer span for each given passage-question pair.

r-NET consists of five layers, which from bottom to top are separately the embedding layer, the encoding layer, the matching layer, the self-matching layer, and the output layer.


Here, we investigate how to incorporate explanations into the learning process to ensure that our model not only makes correct predictions but also makes them for the right reason.

we propose to train a deep model using both ground truth labels and additional annotations suggesting the desired explanation.


In this work, we focus on positive explanations.

we expect the explanation to highlight information that contributes positively towards the label.


Note that event extraction benchmarks contain the annotation of event triggers, which we use to build the annotation Z.

the Z value of every word is annotated to be zero unless it belongs to an event trigger.


Note that for both tasks if an example is negative, its explanation annotation will be all zero.

for negative examples we have C = L. We use simple CNN based models to avoid complexity.


According to Table 2, our method achieves a much higher saliency accuracy for all datasets indicating that the learning was indeed effective in aligning the model saliency with the annotation.

important words will have positive contributions in the saliencytrained model, and as such, it learns to focus on the right part(s) of the data.


Further, it produces models whose saliency is more properly aligned with the desired explanation.

saliency learning gives us more reliable predictions while delivering better performance than traditionally trained models.


However, the baseline model does not pay attention to the contributory words.

the explanation does not support the prediction (unreliable).


We can modify this model for the multiplechoice setting as well.

we use the start P start and end P end distribution to do a weighted sum over the context p to get a summarized representation D start = P start i p i , D end = P end i p i of the context.


To further understand the two different supervision signals, we conduct another experiment where we train using span-based supervision and evaluate on the multiple-choice setting.

during evaluation, we select all document spans that map onto some answer candidate, then max over the scores of all spans to pick the predicted answer candidate.


We construct a string z by prepending lev(x, y) many <mcorr> tokens to y, and construct I such that: We adapt the notation of Mohri (2003) and denote the cost I assigns to mapping a string z to itself as [[I]](z), and set [[I]](z) = ∞ if I does not accept z. SMT(y|x) is the SMT score.

i represents the weighted SMT n-best list after adding lev(x, y) many <mcorr> tokens to each hypothesis as illustrated in Fig.


Bryant and Briscoe (2018) addressed substitution errors such as non-words, morphology-, article-, and preposition-errors by creating confusion sets C(x i ) that contain possible (contextindependent) 1:1 corrections for each input word x i .

they relied on CyHunspell for spell checking (Rodriguez and Seal, 2014), the AGID morphology database for morphology errors (Atkinson, 2011), and manually defined confusion sets for determiner and preposition errors, hence avoiding the need for annotated training data.


Specifically, this table shows that while the trained system was consistently better than the untrained system, the degree of the improvement differs significantly depending on the error type.

since the untrained system was only designed to handle Replacement word errors, much of the improvement in the trained system comes from the ability to correct Missing and Unnecessary word errors.


To this end, we propose novel convolutional self-attention networks (CSANs), which model locality for self-attention model and interactions between features learned by different attention heads in an unified framework.

in order to pay more attention to a local part of the input sequence, we restrict the attention scope to a window of neighboring elements.


In this way, the representations are learned in a hierarchical fashion (Yang et al., 2017).

the distance-aware and local information extracted by the lower SAN layers, is expected to complement distance-agnostic and global information captured by the higher SAN layers.


Besides, our models outperform all the existing works, indicating the superiority of the proposed approaches.

cSANs achieve better performance than Parameter Speed BLEU TRANSFORMER-BASE (Vaswani et al., 2017) 88.0M 1.28 27.31 -+ BI DIREcT (Shen et al., 2018a) +0.0M -0.00 27.58 +0.27 + REL POS (Shaw et al., 2018) +0.1M -0.11 27.63 +0.32 + NEIGHBOR (Sperber et al., 2018) +0.4M -0.06 27.60 +0.29 + LOcAL HARD (Luong et al., 2015) +0.4M -0.06 27.73 +0.42 + LOcAL SOFT  +0.8M -0.09 27.81 +0.50 + BLOcK (Shen et al., 2018b) +6.0M -0.33 27.59 +0.28 + cNNs (Yu et al., 2018) +42.6M -0.54 27.70 +0.39 + 1D-cSANs +0.0M -0.00 27.86 +0.55 + 2D-cSANs +0.0M -0.06 28.18 +0.87 Table 1: comparing with the existing approaches on WMT14 En⇒De translation task.


(2018) forms contextualized word embeddings based on language model representations without adjusting the actual language model parameters.

the ELMo module contains a set of parameters λ 1 .


We replace learned input word embeddings in the encoder network with the output of the language model (SRC-FT).

we use the language model representation of the layer before the softmax and feed it to the encoder.


(2018) relax the above constraint by penalizing the Wasserstein distance between q(z) and p(z).

it is computed by the Maximum Mean Discrepancy (MMD), defined as where P (z) and Q(z) are cumulative density functions.


To alleviate this problem, we propose a simple heuristic that encourages the stochasticity of the encoder.

we penalize, for every data point, a KL term between the predicted posterior q(z|x) = N (µ post , diag σ 2 post ) and a Gaussian with covariance I centered at the predicted mean, i.e., N (µ post , I).


By the chain rule, we have (j)  Notice that k > 0 if we are near a local optimum (locally convex).

the expected gradient of σ i is proportional to σ i .


In the plot, there are 200 buckets in the range of (0, 1).

for some constant C. As known, the KL divergence between two   The constraint in (16) is equivalent to our seemingly heuristic KL penalty optimizes the Wasserstein loss, while restricting the posterior family.


A recent study (Xu and Durrett, 2018) propose to get rid of KL annealing by using the von Mises-Fisher (vMF) family of posterior and prior.

they set the prior to the uniform distribution on a unit hypersphere, whereas the posterior family is normal distribution on the surface of the same sphere.


This shows which hidden vector of the abstract sentence aligned to every region of the concrete sequence.

we see how tools like the big bowl, spoon, and tongs are introduced to facilitate the actions.


To assess the contribution of different components of PretRand, we performed an ablation study.

we successively ablated the main components of PretRand, namely, the learnable vectors (learnVect), the longer training for random units (random++) and the normalisation ( 2 -norm).


To do so, we follow (Tamaazousti et al., 2017) and analyse the units of Φ (biLSTM layer) before and after finetuning.

we compute the Pearson's correlation between all the units of the layer before and after fine-tuning.


6 a concrete example of a biased neuron when transferring from newswire to Tweets domain.

we show the top-10 words activating unit-169 of Φ (from the standard fine-tuning baseline), before fine-tuning (at this stage, the model is trained on the source-dataset WSJ) and during fine-tuning on the TweeBank dataset.


These examples are noisy and biased, but nevertheless they improve lemmatization accuracy on all ten languages both in low (1k) and medium (10k) resource settings.

we showed that context is helpful, both overall and especially on unseen words-the first work we know of to demonstrate improvements from context in a very low-resource setting.


2 The parameters of our probe are exactly the matrix B, which we train to recreate the tree distance between all pairs of words (w i , w j ) in all sentences T in the training set of a parsed corpus.

we approximate through gradient descent: where |s | is the length of the sentence; we normalize by the square since each sentence has |s | 2 word pairs.


We first confirm that our probe can't simply "learn to parse" on top of any informative representation, unlike parserbased probes (Peters et al., 2018b).

eLMO0 and DeCAY0 fail to substantially outperform a right-branching-tree oracle that encodes the linear sequence of words.


In contrast, a complex-valued matching network is built on top of a quantum-theoretical framework for natural language.

an indirect way to measure the distance between two density matrices through trainable measurement operations, which makes advantage of the probabilistic properties of density matrices and also provides flexible matching score driven by training data.


This illustrates the effectiveness of our proposed model from a general perspective.

cNM outperforms most cNN and LSTM-based models, which have more complicated structures and a relatively larger parameters scale.


4 demonstrate that each component plays a crucial role in the CNM model.

the comparison with CNM-Real and FastText-MaxPool shows the effectiveness of introducing complexvalued components, the increase in performance over CNM-Global-Mixture reveals the superiority of local mixture, and the comparison with CNM-trace-inner-product confirms the usefulness of trainable measurements.


We presented an in-depth study on the potential contribution of images for multimodal machine translation.

we analysed the behavior of state-of-the-art MMT models under several degradation schemes in the Multi30K dataset, in order to reveal and understand the impact of textual predominance.


In this appendix, we provide further translation examples for color deprivation (Table 6), entity masking (Table 7) and progressive masking (Table 8).

for the entity masking experiments, we also give further examples to showcase the behavior of the visual attention in Figure 4 and Figure 5.


In order to train a model that understands sentence relationships, we pre-train for a binarized next sentence prediction task that can be trivially generated from any monolingual corpus.

when choosing the sentences A and B for each pretraining example, 50% of the time B is the actual next sentence that follows A (labeled as IsNext), and 50% of the time it is a random sentence from the corpus (labeled as NotNext).


Recent empirical improvements due to transfer learning with language models have demonstrated that rich, unsupervised pre-training is an integral part of many language understanding systems.

these results enable even low-resource tasks to benefit from deep unidirectional architectures.


Instead of relying on protected attributes, our method leverages the societal biases that are encoded in word embeddings (Bolukbasi et al., 2016;Caliskan et al., 2017).


We preprocess these attributes to more easily allow us to understand the classifier's decisions.

we normalize continuous attributes to be in the range [0, 1] and we convert categorical attributes into binary indicator variables.


Our method leverages the societal biases that are encoded in word embeddings of names.

it discourages an occupation classifier from learning a correlation between the predicted probability of an individual's occupation and a word embedding of their name.


One aspect of the Goi-Taikei valency dictionary which we rely on to weight case frames according to expected frequency of occurrence, is the listing of the case frames in order of sense saliency.

the first-ranking entry for a given verb type is the most prominent sense for that verb, the second-ranking entry the second most prominent sense, and so on.


Extracted alternations are represented as the tuple ( [SUFF], [ARGS], SCORE), where SUFF is the directed non-corresponding suffix component between alternates, ARGS is the directed case slot correspondence and SCORE is a numeric statement of the plausibility of the alternation, SCORE E [0, 1].

for example, we represent the alternation (2a) (2b) from above as:5 the verbs for the base and target case frames coincide, the base nominative (ga) case slot is deleted, and the base accusative (o) case slot alternates with the target nominative (ga) case slot.


In the above example, we employed score normalisation, hence the scores of 0.5 for the two alternation candidates from (3b) to 3aOne other area in which the described formulation is lacking, is that it has no way of determining the frequency of each case frame, and hence the relative conspicuousness of any alternations between them.

we want to be able to say that alternating case frames in common use will produce greater salience for that alternation than alternations between rarely-occurring case frames.


The unexpected sparseness of alternation analyses produced by (mi ) may have been due to its inability to pick up on alternations which diverged only slightly in selectional restrictions due to annotational inconsistency in the dictionary.

it may be the case that what should have been recognised as alternations and hence have matching selectional constraints were overlooked, a possibility we consider by way of (m2).


This study looks at language differences found in the conversations of two groups of Japanese women differentiated by their age.

it investigates differences in the strategic use of such aspects of speech as personal pronouns, adverbs, sentence-final particles, and fillers and words of habit.


The informants were all 'part-time' workers.

they worked from 9 a.m. until 5 p.m., but did not have the same contractual obligations and/or rights as 'full-time' employees.


This paper presents an automatic method for evaluating the spoken language skills of second language learners.

we describe a proficiency test called SET-10, developed by Ordinate Corporation, that measures a person's facility in spoken English.


The first type is based on the correctness of the content of the response, that is, a judgment of the number of errors based on the exact words spoken in the responses.

did the candidate understand the item and provide a timely and correct response.


The items present a quasi-random sample of phrase structures and phonological styles that occur in spontaneous conversation, while restricting the vocabulary to the high-usage sector of the vernacular lexicon.

the requirement that at least 90% of educated adult native speakers perform correctly on every item suggests that the tasks are within the limits of the performance domain.


The correlation coefficient for this data is 0.94, which compares well with the single-rater reliability we observed for the human-rated Overall score of 0.94.

the machine grades agree with the aggregate human judgments about as well as single 8 00 human raters agree with the aggregate human judgment.


Collocation is a technique that originates from a particular distributional feature of lexical cohesion, namely that the number of links shared by segment pairs tends to increase as the distance between segments decrease.

lexical items that occur together regularly are said to be collocated.


We make use the shortest path in the is-a hierarchy to measure the conceptual similarity between the tokens in the discourse segments (Rada et al., 1989).

given tokens w and w' in the is-a hierarchy, the distance between the tokens is as follows: distance(w , w') = minimal number of is-a relationships between w and w' An is-a hierarchy in the Chinese thesaurus, as in other thesaurus such as WordNet (Miller, 1985), can be viewed as a directed acyclic graph with single root.


It is thus addressees who are responsible for providing the relevant information.

the requirement of information contribution is obviated on the part of the questioners and the question words need not bear any new information.


While ba still shares its subject with the lexical verb in the object-retained type, the object is no longer shared.

each of the verbs in (2) has its own object (the object of ba is san ge taozi 'three peaches', while the object of chi 'to eat' is Jiang gè two').


On the other hand, the resultative verb requires that the verbal complement as a whole possess 'resultative' in SEMTYPE regardless of the lexical specification of SEMTYPE for the embedded verb.

sEMTYPE may be used as a device to signify whether a resultative meaning is present in a verb phrase.


• It follows that there is an object u in w which satisfies `piza( t)' in every world wherè watashi' is relevant.

it satisfies it at w. • Note that since `watashi' is not singular it need not be .


We can consider objects existing, apart from other predicates they might haver' • So ]tal t watashi aru ( ) w ie (")] is satisfied at when in every world where `watashi' wa is relevant, there is a u which is a house in every house-relevant world that satisfies ]xaru(x)'.

there will be a house in every world where `watashi' is relevant.


and thus axie(x) must also be satisfied.

there must be a house at every world in which `watashi' is relevant.


Thus, the two disjunctive situations in R, namely R, and R2, corresponds to indeterminacy of possibilities that are associated with the two situations shown in (18) above.

use of disjunction with "ka" which expresses indeterminacy among two potentials is quite informative in such a situation, because this indeterminacy relates directly to the following course of events.


In cases where the whole set of passengers passed through either the west gate or the east gate, this sentence would not be felicitous.'

disjunction in (19) shows distribution into the set of passengers from the west gate and the set of those from the east gate.


Bresnan and Kanerva 1988, Zaenan 1993, Pustejovsky 1995.

lexical semantics not only can be empirically verified with grammatical predictions but can also be justified by conceptual arguments.


This last premise is especially important because it restricts the type of evidence that may be brought to bear on the question of whether something shares a particular attribute or not, and limits the possibility of ad-hoc explanations.

it strongly focuses analyses in verbal semantics on corpus-based approached, since representational clues are best extracted from corpora.


That is, it strongly focuses analyses in verbal semantics on corpus-based approached, since representational clues are best extracted from corpora.

in our work on lexical semantics we have concentrated on exploring the semantic and syntactic differences between near synonyms in the Sinica Corpus.


Our goal is to locate the linguistic relation that defines the contrast.

we look for the semantic relation that can predict the difference in grammatical behaviors of the set.


The second important hypothesis of this proposal is that the event representation of a verb is the sum of all attested event realization of a particular the verb.

it is possible that a complex lexical event representation is never fully instantiated; although each component is linguistically attested.


In this section, we will concentrate on the basic building blocks of our verbal semantic theory.

we will propose a theory where event structures can be composed from a small set of event modules and the backbone of verbal semantics is taken to be compositions of these event modules.


We assume that conceptual and cognitive motivations entail that certain event module combinations be perceived as a whole, and thus be mentally and linguistically represented by a single event structure with compositional modules.

we are proposing a non-homomorphism Composite events involve more than one nucleus event and may or may not be bounded.


is its aspectual feature of achievement ill the sense of Dowty(1979).

we can no longer recognize the original sense "to become" of nar in SHF.


First of all, the target NP can alternatively be an argument of the predicate.

though the target NP cannot occur in the IHRC as it is, it can occur alternatively as an argument of the same predicate.


Given that the availability of the implicit target of IHRC is observed for sentences in (2) as well as those in (10), the necessary involvement seems to be the real key to the licensing of the implicit target.

what is crucial is not whether the implicit target can appear as an alternative argument of the predicate: it is whether the target is a necessary part of the meaning of the predicate.


Comparing the sentences that allow IHRC and those that do not, this contrast results from the event structure of the predicate and the way the implicit argument is involved.

the IHRCs in (2), (5), (6), (10) are all headed by an accomplishment verb and the implicit target figures in the resultant state designated by the verb.


There could be other activities involving a book, but these two are most readily available as interpretations of the phrase.

the range of possible interpretations of the type shift in question is constrained by certain aspects of the noun book; it is usually limited to interpretations which concerns how it comes into being and how it is used.


However, the implicit target, what is scraped off, is not the sardine itself but the burnt substance attached on its surface.

the formal role needs to be sensitive to the semantic decomposition of x being burnt into there being burnt substance on the surface of x.


In Japanese, the expected reading is available only in the form of the IHRC.

otherwise, the container cannot refer to its content: the container could refer to its content only when the semantic extension and decomposition operate, making the content accessible.


A toy box and a box of visiting cards could contain something other than toys and visiting cards, respectively, and yet the IHRC reading is available only referring to toys and visiting cards So it may seem that the not-entailed semantic decomposition involving an argument applies only when the decomposition makes accessible the lexically-specified part of the argument.

it may seem that the implicit target must be lexically-specified in some ways.


I did not discuss how the integration can be technically achieved, or how much the semantic decomposition is allowed to go.

4 The solution of these problems awaits further investigation of the technical details of GL, as well as examination of more data it is not even very clear at the moment in what level of semantic decomposition the basic representation of the quale (including the formal role) should be given.


The model of GL by Pustejovsky is employed to represent these restrictions.

the qualia structure is shown to capture the availability of implicit target of IHRC.


Secondly, the undeleted old arguments in (1B2) and (1 B3) undergo some kind of structural changes.

minsu in (1B2) and chaek 'book' in (1 B3) are moved from their canonical position to the sentence initial position, and the topic marker nun is attached to them.


Chierchia (1984Chierchia ( , 1985 argues that properties, the meanings of VPs, have two forms or modes of being: propositional functions and individual correlates of propositional functions.

the expression denoting a property can be realized either as a finite predicate phrase or as its nominalization.


As shown, nearly 70% of the occurrences of big in BNC are used to refer to concrete objects, but only 55% in TLCE.

the Taiwanese learners use the word big much more often than native speakers do when describing abstract concepts.


The next question is how the focus difference between the two constructions is reflected in discourse.

under what sort of condition is object topicalization used in place of the passive?


In fact, the idea of symmetric projection is not incompatible with the fundamental assumptions in the minimalist program.

it is argued in [2] that the simplest object constructed from two lexical items a and p is the set {a, 13 }, where a and p are the sets of their component features.


The label of the object consisting of the two items could be the union of their features in this case.4 Going back to (1)-(3), the two bracketed phrases in each should be analyzed as having the same internal structure.

if ka is assumed to have no category feature, the labels of the two bracketed phrases in (1) repeated below are the unions of the features of the wh-phrase and those of ka: ( the ka-phrases are both categorially NPs due to the category feature of the inside noun dare.


Licensees and licensors are features involved in movement.

a noun phrase has a licensee case feature, and a transitive verb has the corresponding licensing feature +case.


As has been discussed in Section 2, phrasal interrogatives of this kind require discourse-level information for their full interpretations.

the second conjunct of (1) will ultimately be interpreted on a par with the full sentence below: (27) [ John-ga dare-o nagutta ka sitteiru hito wa i nai.


I'll list all the adverbial connectors found in my data and argue that even representing the same adverbial category, these adverbials are not at equal status in "frequency".

take when-clause for example, although there're three possible expressions as dang ... (de shihou) vs. zai...de shihou vs. ...de shihou, their frequencies in Chinese spoken discourse are significantly different.


Note that the conditional connectors listed in table 3 by my data, I have to confess, do not include all the possibilities in Chinese.

unlike English, Chinese has abundant conditional adverbial connectors, and even there is a slight distinction between "colloquial" and "literary" usages.


In section 2, I will discuss some basic properties of alternative questions.

it is pointed out that an analysis of alternative questions should explain the fact that questions like (1) may receive a polar question interpretation as well as an alternative question interpretation.


Following Ginzburg [2], we will treat cases like (4) as disjoined polar questions.

each disjunct in (4) is a polar question and is connected by or to form a coordinated sentence.


The use of 0, however, is not fully motivated.

it is not clear why disjunction in English always needs a syntactic scopal indicator, while other scopal elements such as quantifiers and negation do not.


However, this view, based on the tacit assumption that the two tenses have some essential properties in common, leaves the relationship between them totally vague.

it has not been made clear whether one and the same semantic representation can be posited for each of the two tense forms irrespective of their syntactic position, or two or more representations are necessary.


This means that the principle can be relativized to any of these notions, none of which can be arbitrary, otherwise the principle would be formally void.

groupings of constituants, simple or complex, into more complex constituants not only cannot be arbitrary but can also be tested relative to their compatibility with the principle of compositionality.


Now we can also build up a rough semantic generalization of Table-1 of Korean PSIs.

we can characterize the licensing condition of amwu-N-to/-irato/-ina in terms of "(non/anti-)veridicality" as in the following table:  We should point out that there are a few exceptions to the above generalization: First of all, as shown in Table-1, amwu-N-to is allowed in before-clauses and eps-/molu-'not exist/know,' which are not exactly antiveridical but more negative than conditionals or comparatives.


Their distribution also raises a big conceptual problem against previous semantic proposals on the licensing conditions of negative polarity items.

(Zwarts 1990(Zwarts , 1993Nam 1994;Giannakidou 1998) amwu-N-irato is licensed by non-veridical contexts but, contrary to their claims, it is not licensed by more negative, antiveridical contexts.


The morpheme amwu in Korean carries a quantificational force of "arbitrary choice," which roughly denotes 'no matter what object x it may be.'

given a domain of discourse D, amwu-chooses an arbitrary object in D, so to be a kind of choice function.


Due to its functional nature of arbitrary choice function, amwu does not refer to an entity of a given domain, and so does not introduce a "discourse referent" (Kamp & Reyle 1994) to the relevant discourse structure.

(10b) and (11b) are not a natural discourse, since the pronoun ku (saram) cannot be resolved in the discourse.


Then,(16) implies that `Jini is the least likely person who can do such a thing.'

we just note here the difference between Jini-to and Jini-na in (16): when Jini-to/-rato is used in (16), the speaker of (16) expects that 'there are other (relatively many) people than Jini who are likely to be able to do such a thing,' but when Jini-na is used, the speaker expects that there are relatively few people who is likely to be able to do such a thing,' and that Jini is the least likely person who is likely to do it.


In contrast, amwu-na reads as 'anybody, irrespective of the quality he has'.

amwu-na is an arbitrary choice in the quality scale.


Pesetsky's claim is based on the zero morpheme, but his argument cannot account for the -er nominals in (5) (See Chung 1998 for the detailed discussion).2 Bouchard 1995 just assumes that the E0 verbs have external argument without presenting evidence for the external argument of the EO verbs.

iwata 1995 presents some pieces of evidence (i.e., middle formation, -er nominals, -able adjectives) that the EO verbs are simple transitive verbs: the verbs have external and direct internal argument.


Thus, they argue that the passive in (20) is an adjectival passive.

but I will show that this argument does not save them but the passive is a counter-evidence to them; it shows that the EO verbs have external argument.


Since the act of demonstration requires an audience, most analysts would include the to-phrase (to the students).

a lexical description of the verb must say that the subject and these two syntactic positions function in identifying frame elements that are necessary parts of the concept of the frame evoked by it.


• For nouns which occur with support verbs, we annotate constituents capable of being seen either as complements of the noun or as complements of the verb.

unlike a parser, we don't have to decide the correct constituent structure of a sentence like She registered a complaint to the committee about her treatment by your colleagues.


What we would like to determine in this paper is if similar contrasts be found in near synonyms of other languages.

are event modules in other languages (such as English) organized along similar conceptual lines?


In this paper, we examine this theory in light of the English data.

we will look at the near synonym contrast of the verbs 'put' and 'set' based on data from the two million word sampler of the British National Corpus (BNC).


It cannot be interpreted because the theme 'water' is not given any definite position in the location.

the example in (7b) indicates that although a bounded space is not required for 'set', a position of theme is needed.


The most salient property of else in comparison with other items appearing in the exceptive constructions is that else normally does not overtly mark the "exception".

the "exceptional" entities are not overtly realized'.


In this section, I add another peculiar property else exhibits.

two instances of else in the same sentence induce pair quantification.


We consider the computational implications of three different implementations.

we examine the effect on bottom-up parsing from varying the Specifier-Head-Complement order.


The contribution of particular elements of a theory to computational complexity can be determined through experimentation.

we will discuss the effects of variation in Specifier-Head-Complement order, and the contribution of empty heads and prefixes to the complexity of morphology within the framework of bottom-up shift-reduce parsing.


Computerize is analyzed as the noun computer followed by the suffix -ize, which is, in turn, further decomposed into -i(z)-e, where the two constituents are merely instantiations of the (abstract) inchoative and causative morphemes, shown earlier in (24).

as 29 the only difference with bottle as a verb is that the inchoative and causative morphemes are zero affixes.


The difference between (2b) and (3b) is the presence or absence of the possibility operator.

(2) commits the speaker only to the possibility of somebody other than Taro also having come, whereas (3) to its truth.


In (12b), the ratified topic Taro wa should be deleted unless the intended reading is a contrastive one such as 'Taro indeed went to the cinema, but I don't know about the others.'

wa as the marker for topic ratification turns into an EP with a contrastive meaning when it occurs in a context where the self is already a ratified topic (Lambrecht and Michaelis, 1998).


(13) Taro-sika ringo-o tabe-nai Taro-SIKA apple-OBJ eat-NEG Only Taro eats an apple.'

14Taro-sika ookina-kemusi-no iru ringo-o tabe-nai Taro-SIKA big-worm-be apple-OBJ eat-NEG Only Taro eats an apple which a big worm is in' (15) Taro-sika kasikoi-tori-kara kakureta ookina-kemusi-no iru ringo-o tabe-nai Taro-SIKA wise-bird-from hiding big-worm-be apple-OBJ eat-NEG Only Taro eats an apple which a big worm that is hiding from a wise bird is in' sika construction can embed another sika construction, as can be seen in the following: (16) Taro-sikal Ken-ga eigo-sika2 ie-de hanas-anai2 koto-o sir-anail Taro-SIKA Ken-SUBJ English-SIKA house-at speak-NEG fact-OBJ know-NEG Only Taro knows that Ken speaks only English at home' Taro-sika is interpreted as associated with sir-anai, hence the same superscript, while the internally embedded eigo-sika is interpreted as associated with hanas-anai.


Given a proposition co that has the positive polarity, namely co+ , its negation := has the negative polarity; we represent this as 1)01() = -, or Using this notation, we can state the principle of double negation: Given a proposition with the negative polarity (p-, its negation //) := ---,co has the positive polarity, namely 0+.

this principle of double negation implies that and co have the same polarity, and so do and the polarity "flip-flops" depending on whether the number of negations is even or odd.


All subjects were born in Taiwan and their first language is Taiwanese.

this set of data is on Taiwanese accented Mandarin.


Across all three dialogues, given a specific discourse particle, the sentential position where it is located is usually the same.

there is a regular mapping between the types of discourse particles and the positions where they are to be found.


(ou), ' i (hai), and , 01022 make up 5%, whereas ni-zhê words MI (na), Lt414--(zhe yang zi)23 and it* (zhê yang) are 6.6%.

these 12 words highlight the beginning of more than 40% of turns in our data.


The extra-linguistic knowledge that the speaker is male may be indicated by describing the fact with some conjunction like where: e. where male( ) When the extra item is added as in (5), the effect is usually taken into account by restricting the domain where the semantic representation should be interpreted.

the representation should only be interpreted against a domain in which the speaker is male.


per son(i) When combined with the set of axioms (4), the axiom turns the give semantic representation into the ohter below: aru I (vehicle(x) 3.4 Points to note We ensure that the exponential, !, should not distribute under any other connectives but the multiplicative one.

we allow for the consturctions such as !p q but not for other forms of constructions such as rVs where the exponential is put under the other connective, \.


This is called the principle of END-FOCUS (Quirk et al., 1985(Quirk et al., )(p.1357.

the change of information value in English text has a regular pattern, which is shown by Figure 3.


Kager (1993) argues that initial parsing feet and surface feet must be discussed at different levels.

exhaustivity may be satisfied by parsing units into surface units and may be relaxed at the initial parsing level.


This paper investigates what words map from the source domain of PLANT in Mandarin Chinese.

we examine how different aspects of the source domain of PLANT are mapped onto the different target domains of LOVE, MARRIAGE, HAPPINESS, and BELIEFS.


We find that different aspects of plants are selected by different target domains.

mPs allow us to see why a particular target domain selects a range of source domains.


Third, as all selects a VP or AP via its MOD feature, it appears as a modifier of a VP or AP in the structure.

all, as a adjunct daughter, combines with a VP or AP, constituting a headadjunct phrase.


Focusing some element in a sentence amounts to making a statement to the effect that some predication holds of the object that corresponds to the focused element in some other circumstances.

focusing an element in a sentence is to give a particular condition in which the predication holds with the object in question.


Thus, the additive operator "mo" adds the object a which takes a lower value of likelihood compared to the set of objects fis each of which takes a higher value.


San-nin-mo, on the other hand, indicates the number in question (namely, three in this case) is beyond expectation, and therefore may be translated into "as many as three (people)."

wa adds an implication that the number is minimum, and mo implies that the number is excessive with respect to a certain standard.


Sentence (2), where the two NPs, unhayng 'bank' and ton 'money', have the same ANIMACY value, could be a challenge to their theory.

though the PC Constraint does not apply to (2) because of the different PC values of the two NPs, the RC & ANI Constraint wrongly predicts that the two NPs cannot be scrambled because they have the same RC and ANIMACY values.


Our analysis correctly predicts that (20b) is ungrammatical by the definition of the RM & ANI Constraint.

the two argument NPs cannot be scrambled because they share the same RM and ANIMACY value, i.e.


The idea developed in this paper was conceived from Kim (2000) and discussions with many people.

i would like to thank Chung Chan, Byung-Soo Park, ivan Sag, Peter Sells, among others.


This means that in principle, there can be four different types of languages in terms of WH-pied-piping.

i show that Basque and imbabura Quechua are languages where both the direct and indirect WH-feature checking movement are overt while WH-in-situ languages like Sinhala and Korean are those where both the direct and indirect WH-feature checking movement are covert.


Of these four types of languages, what is novel and deserves discussion is the possibility of languages where the nature of the direct and indirect feature checking movement differs, i.e., the languages where the direct WH-feature checking movement is overt while the indirect WH-feature checking movement is covert and the languages where the direct feature WH-checking movement is covert, while the indirect WH-feature checking movement is overt.

the predictions for these two types of languages is that a constituent can be pied-piped overtly by a WH-element which is not in the Spec on the surface (first type) and that WH-elements can appear in non-scopal positions without any overt scope markers (second type).


The same is also true in Basque according to Ortiz de Urbina (1993).

in my analysis clausal pied-piping in these languages is explained in the following way: On its way to the Spec of +WH-C, the WH-phrase will pass through the Spec of intermediate CPs due to Subjacency.


Of course, given that feature percolation is possible only from the Spec (and the Head), in order for this feature percolation and the subsequent pied-piping movement to be possible, the WH-phrase such as kau d 8 must move 'covertly' to the Spec of the pied-piped phrase.

as for the Complex NP island violation cases such as (24a), I will assume, following Nichigauchi (1992) and Ortiz de Urbina (1993), that relative clauses take up the Spec(DP) position in languages like Sinhala, Japanese, Korean and Basque, which do not show Complex island effects.


The prediction for this type of pied-piping language is that WH-phrases can appear in non-scopal positions without any overt scope markers such 11 See Yoon (1999) for more discussion on this.

12 if syntactic islands such as adjunct islands can be pied-piped in these languages, the predicion is that the WH-phrase will appear in the initial position of the island although the island itself stays in-situ.


All these constructions are related with each other in a hierarchy so that subtypes may inherit all the properties of their supertypes.

they are fit into a multiple inheritance hierarchy like (12) given by Malouf (2000:15), for example.


The GP is a noun-possessive-cx because of the hierarchical fact that gerund is a subtype of noun as defined in the type hierarchy (4).

the GP satisfies the condition that the category of the noun-possessive-construction must be a noun, because this condition subsumes the fact that the construction is a gerund.


I draw on Yoon's (1993) concept of R-relation (which of course needs revision for IHRC) to circumvent the problem.

the main predicate does not directly refer to a specific entity of the complement clause; it refers to the event denoted by the clause, which is indirectly associated with the entity.3 Undeniably, IHRC has a peculiar structure.


The semantic difference between IHRC and EHRC is particularly evident when the target is modified by, quantifiers and certain types of other modifiers (Shimoyama 1999).

kim's (1999) analysis licenses IHRC (of entity reading) as a full-fledged relative clause, and therefore fails to capture any peculiarity of IHRC as a relative clause.


The distinction between clausal complement and IHRC is made not by a morphological or syntactic clue of any sort but by the selectional restriction of the main verb.

the apparent clausal complement counts as IHRC when the main verb requires an entity rather than an event as its argument.


That is to say, from the same ARG-ST, a clausal complement obtains if it is directly mapped to VAL features through ARP, an adverbial clause if it goes through Argument Extension before ARP, and an IHRC if goes through a lexical rule which simulates the Argument Extension while overriding ARP.

it is proposed that the licensing of IHRC is endorsed by the defeasibility of ARP, which has been tacitly maintained undefeasible cross-linguistically Wasow 1999, Bouma, et al.


From this consideration it is suggested that the development from clausal complement through IHRC to adverbial clause embodies the loss and regain of stability.

the recurring development from IHRC into the adverbial clause is motivated by the strong quest for stability.


Treating verbal gerunds syntactically as verb categories, this paper proposes that there are cases of control involving gerunds as in infinitives.

this paper claims that control is not limited to gerundive complements of verbs but can be extended to prepositional complements.


e. Well, we will need to look at if we're going to be selling The data (14) shows that verbal categories can be a prepositional complement.

verbal categories can be a complement of a preposition.


Several types of questions are experimented to test the effectiveness of the algorithm here proposed.

questions of the form why are treated to show how causality reasoning works.


It is clear that the interpretation of the sentence is crucially dependent on what is the focused phrase.

the focused phrase is what the FP only is interpreted with respect to.


Since the aim of this paper is to give an explicit account of syntactic and semantic aspects of FPs and association with focus phenomenon in Japanese, it would be convenient for our present purposes to call what the FP semantically associates with the focused phrase.

focus is defined here entirely in terms of semantics, the realization of which in the phonological domain is assumed to be at most optional.3 One major difference between a Japanese FP and an English one is that the former sometimes allows association with phrases larger than the one it attaches to, while the latter never allows it.


The crucial point here is that these two sentences, in which the FP is placed inside the embedded clause, cannot, in any of their interpretations, be equivalent to (6c), in which the FP is in the matrix clause.

if the FP in (6a) were to associate with the matrix object NP which contains the whole embedded clause inside itself as a relative clause, the interpretation of this sentence would turn out to be equivalent to one of the several possible interpretations for (6c) in which the FP associates with the NP it attaches to.


With a phonological pitch accent on the noun baiorin, (8) is obligatorily interpreted in the reading in which this noun is focused and associated with mo.

in this case the sentence unambiguously means that we will look for a vocalist who can play the violin besides vocalists who can play other instruments.


This should be taken as the formal definition of the term "focus" in the present account.

we say that a particular phrase is focused if the LOC value of that phrase is structure-shared with the object in the FOC set of that phrase.5 Putting aside for the moment the precise definitions of where the FOC value6 El comes from, how its percolation takes place and terminates at an appropriate node, what is happening in the tree (11) should be clear.


The interpretation of the FP has to be carried out within the minimal clause containing the FP.

the Focus Resolution Rule has to be applied in the same clause as the FP is in.


This requirement can be formulated as follows: This constraint ensures that the SUBCAT list of any given sign cannot contain as its member an S ([HEAD verb, SUBCAT ()]) which has an [ID +] FOC value.

no subcategorized clause can inherit an [ID +] FOC value from a phrase inside it.


When the focus particle dake attaches to this I5Notice that the structure-sharing between the LOC value and the value of the item of the FOC feature rules out a recursive application of the Focusing Rule.

once this rule is applied, the LOC value of the mother node is different from that of the daughter node with respect to the CONT feature.


as shown in (16) the argument mapped onto the passive subject is excluded because it is independent of the causing event.


Since the determination of the reality status of an event involves a judgment, the status operator can introduce another level of clausal structure corresponding to the judgmental event.

the status operator can modify a tensed clausal unit, and can itself be modified by the tense operator.


Etymologically speaking, iru is derived from wiru, which means that a potentially mobile object is at a standstill.

the state of existence at a location is just temporary in the case of iru.


The following Generative Lexicon representations capture this commonality and their differences.5 When they are used as component verb in the V-te aru/iru constructions, they also subcategorize for the event denoted by the V. This argument corresponds to ARG2 of their full verb counterparts.

instead of locations, the component verbs aru and iru subcategorize for events.


The state in which the subject finds himself/herself is the one which results from the completion of the event in question.

the resultative meaning can come for free without using this construction as shown in (23b).


So in weak symmetry form, the similarity between documents in one language is the same with those of the matching documents in another language.

though corresponding documents in different language may be quantified differently, for example in vectors of different dimensions, we can simplify LSI approach as long as the consistency of similarity relationships between documents of the same language are preserved across languages.


the probability of the juncture between WC and its previous word wp=ek ek+i wel , it can be formulated as PWJM -0 (WC Pr(t B(wpwc)), if both wp and WC are known Pr (tB(ei ed), if both wp and WC are unknown Pr (tB (ei wc)), if wp is unknown and WC is known Pr (tB (wped), if wp is known and WC is unknown 32.2 Word-formation patterns As we can see, a segmented word may be an independent lexicon word or a combination of lexicon words.

a lexicon word presents itself as one independent word or one component word of another word after segmentation.


2000), here Count(w) refers to the total frequency of the character string corresponding to the word w in the training corpus, which is counted from word lattice or from raw corpus of the relevant training data, and Count(pttn(w)) is the frequency of the word w given its certain pattern pttn(w) , which can be counted directly from a segmented corpus.

if we encounter an unknown word in process of counting, we firstly segment it into a sequence of component words using the forward maximum match method, and then count the relevant pattern frequency for each component word.


This section describes an algorithm for integrated segmentation.

a filter algorithm is given in detail in this section to enhance the precision and avoid the problem of combination explosion in the construction of candidate words.


In our opinion, whether a character string should be blocked from becoming an eligible word candidate depends not only on the word-formation power of each component itself but also on the internal juncture probabilities between these components.

we employ the word-based word-formation power and the internal word juncture probability to capture these features and define three conditions for filtering these ineligible candidates in our algorithm.. Let w= el e2 • el denote a potential word candidate, then these conditions can be formally defmed as followings: (1) Word-length condition: If w is an eligible word candidate, then its length 1w f< TL .


min (Pwjm_i (el , e2 ), -, Ppm _I e. e. ), ---, Pwn I _I (e1_1,e1))> Twjm • As a group, these conditions serve as a sufficient condition for constructing unknown word candidates.

if a character string is an eligible unknown word candidate, it must satisfy the three conditions simultaneously.


HPSG is a constraint-based, lexicalist approach to grammatical theory that seeks to model human languages as systems of constraints on typed feature structures.

the grammar adopts the mechanism of type hierarchy in which every linguistic sign is typed with appropriate constraints and hierarchically organized.


In addition, all the verbal suffixes are optional except the mood marker.

for a verb stem to appear in syntax, it should be inflected at least with a mood marker (cf.


We suspect that as the grammar port moves beyond core syntactic structures to more "peripheral" ones, there will be further changes to the annotated phrase structure rules that involve more than morphosyntactic differences.

korean allows adverbial sentential negation as well as the suffixal negation found in Japanese and allows certain double accusative constructions which are impossible in Japanese.


It is only the transition of the contour that defmes this Figure.

the most salient cognitive feature in this abstract space is the contour.


This is represented in the above cognitive account of the prominence of transition.

a homogenous area does NOT attract visual attention.


The main point is that Safir's PCOB is a constraint on A'-binding and not on A-binding.

japanese ga-marked subjects function as A'-binders.